AIMC Topic: Algorithms

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Volumetric Medical Image Segmentation Through Dual Self-Distillation in U-Shaped Networks.

IEEE transactions on bio-medical engineering
U-shaped networks and its variants have demonstrated exceptional results for medical image segmentation. In this paper, we propose a novel dual self-distillation (DSD) framework in U-shaped networks for volumetric medical image segmentation. DSD dist...

Adaptive Cardiorespiratory Separation With Harmonic Models and Filters: The Case of Electrical Impedance Tomography.

IEEE transactions on bio-medical engineering
Cardiorespiratory monitoring methods are vital in clinical and personal healthcare contexts, continuously delivering comprehensive insights into patient health. Among them, electrical impedance tomography, a non-invasive imaging modality, uniquely en...

Migration of Deep Learning Models Across Ultrasound Scanners.

IEEE transactions on bio-medical engineering
A transfer function approach has recently proven effective for calibrating deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquisition and machine levels. Expanding on this approach, we develop a str...

CLaI: Collaborative Learning and Inference for Low-Resolution Physiological Signals: Validation in Clinical Event Detection and Prediction.

IEEE transactions on bio-medical engineering
While machine learning (ML) techniques have been applied to detection and prediction tasks in clinical data, most methods rely on high-resolution data, which is not routinely available in most Intensive Care Units (ICUs), and perform poorly when face...

A Neighbor-Sensitive Multi-Modal Flexible Learning Framework for Improved Prostate Tumor Segmentation in Anisotropic MR Images.

IEEE transactions on bio-medical engineering
Accurate segmentation of prostate tumors from multi-modal magnetic resonance (MR) images is crucial for the diagnosis and treatment of prostate cancer. However, the robustness of existing segmentation methods is limited, mainly because these methods ...

Hierarchical Dynamic Graph Convolutional Network With Interpretability for EEG-Based Emotion Recognition.

IEEE transactions on neural networks and learning systems
Graph convolutional networks (GCNs) have shown great prowess in learning topological relationships among electroencephalogram (EEG) channels for EEG-based emotion recognition. However, most existing GCN-only methods are designed with a single spatial...

Risk Prediction of Low Bone Density in Elderly Patients with Supervised Machine Learning Algorithms.

Balkan medical journal
BACKGROUND: Low bone mineral density (BMD) is a common age-related condition that elevates the risk of fractures and mortality. Machine learning (ML) techniques offer a promising approach for early prediction using readily available clinical, biochem...

Prediction of mortality in cancer patients with COVID-19 using machine learning methods.

Medicine
This study aimed to predict mortality in cancer patients diagnosed with COVID-19 using machine learning (ML) algorithms and identify the clinical and laboratory parameters associated with mortality. Demographic, clinical, and laboratory data of cance...

Cardiac amyloidosis detection from a single echocardiographic video clip: a novel artificial intelligence-based screening tool.

European heart journal
BACKGROUND AND AIMS: Accurate differentiation of cardiac amyloidosis (CA) from phenotypic mimics remains challenging using current clinical and echocardiographic techniques. The accuracy of a novel artificial intelligence (AI) screening algorithm for...